The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences

The study of dreams represents a crucial intersection between philosophical, psychological, neuroscientific, and clinical interests. Importantly, one of the main sources of insight into dreaming activity are the (oral or written) reports provided by dreamers upon awakening from their sleep. Classically, two main types of information are commonly extracted from dream reports: structural and semantic, content-related information. Extracted structural information is typically limited to the simple count of words or sentences in a report. Instead, content analysis usually relies on quantitative scores assigned by two or more (blind) human operators through the use of predefined coding systems. Within this review, we will show that methods borrowed from the field of linguistic analysis, such as graph analysis, dictionary-based content analysis, and distributional semantics approaches, could be used to complement and, in many cases, replace classical measures and scales for the quantitative structural and semantic assessment of dream reports. Importantly, these methods allow the direct (operator-independent) extraction of quantitative information from language data, hence enabling a fully objective and reproducible analysis of conscious experiences occurring during human sleep. Most importantly, these approaches can be partially or fully automatized and may thus be easily applied to the analysis of large datasets

Deployment and operational aspects of rural broadband wireless access networks

Broadband speeds, Internet literacy and digital technologies have been steadily evolving over the last decade. Broadband infrastructure has become a key asset in today’s society, enabling innovation, driving economic efficiency and stimulating cultural inclusion. However, populations living in remote and rural communities are unable to take advantage of these trends. Globally, a significant part of the world population is still deprived of basic access to the Internet. Broadband Wireless Access (BWA) networks are regarded as a viable solution for providing Internet access to populations living in rural regions. In recent years, Wireless Internet Service Providers (WISPs) and community organizations around the world proved that rural BWA networks can be an effective strategy and a profitable business. This research began by deploying a BWA network testbed, which also provides Internet access to several remote communities in the harsh environment of the Scottish Highlands and Islands. The experience of deploying and operating this network pointed out three unresolved research challenges that need to be addressed to ease the path towards widespread deployment of rural BWA networks, thereby bridging the rural-urban broadband divide. Below, our research contributions are outlined with respect to these challenges. Firstly, an effective planning paradigm for deploying BWA networks is proposed: incremental planning. Incremental planning allows to anticipate return of investment and to overcome the limited network infrastructure (e.g., backhaul fibre links) in rural areas. I have developed a software tool called IncrEase and underlying network planning algorithms to consider a varied set of operational metrics to guide the operator in identifying the regions that would benefit the most from a network upgrade, automatically suggesting the best long-term strategy to the network administrator. Second, we recognize that rural and community networks present additional issues for network management. As the Internet uplink is often the most expensive part of the operational expenses for such deployments, it is desirable to minimize overhead for network management. Also, unreliable connectivity between the network operation centre and the network being managed can render traditional centralized management approaches ineffective. Finally, the number of skilled personnel available to maintain such networks is limited. I have developed a distributed network management platform called Stix for BWA networks, to make it easy to manage such networks for rural/community deployments and WISPs alike while keeping the network management infrastructure scalable and flexible. Our approach is based on the notions of goal-oriented and in-network management: administrators graphically specify network management activities as workflows, which are run in the network on a distributed set of agents that cooperate in executing those workflows and storing management information. The Stix system was implemented on low-cost and small form-factor embedded boards and shown to have a low memory footprint. Third, the research focus moves to the problem of assessing broadband coverage and quality in a given geographic region. The outcome is BSense, a flexible framework that combines data provided by ISPs with measurements gathered by distributed software agents. The result is a census (presented as maps and tables) of the coverage and quality of broadband connections available in the region of interest. Such information can be exploited by ISPs to drive their growth, and by regulators and policy makers to get the true picture of broadband availability in the region and make informed decisions. In exchange for installing the multi-platform measurement software (that runs in the background) on their computers, users can get statistics about their Internet connection and those in their neighbourhood. Finally, the lessons learned through this research are summarised. The outcome is a set of suggestions about how the deployment and operation of rural BWA networks, including our own testbed, can be made more efficient by using the proper tools. The software systems presented in this thesis have been evaluated in lab settings and in real networks, and are available as open-source software

Environmental Genomics: A Tale of Two Fishes

The influence of the environment on two congeneric fishes, Gillichthys mirabilis and Gillichthys seta, that live in the Gulf of California at temperatures of 10-25 degrees C, and up to 42-44 degrees C, respectively, was addressed by analyzing their genomes. Compared with G. mirabilis, G. seta showed some striking features. Substitution rates in the mitochondrial genes were found to be extremely fast, in fact faster than in noncoding control regions (D-loops), from which a divergence time of less than 0.66-0.75 Mya could be estimated. In the nuclear genome, 1) both AT --> GC/GC --> AT and transversion: transition ratios in coding sequences (CDSs) were relatively high; moreover, the ratios of nonsynonymous/synonymous changes (Ka/Ks) suggested that some genes were under positive selection; 2) DNA methylation showed a very significant decrease; and 3) a GC-rich minisatellite underwent a 4-fold amplification in the gene-rich regions. All these observations clearly indicate that the environment (temperature and the accompanying hypoxia) can rapidly mold the nuclear as well as the mitochondrial genome. The stabilization of gene-rich regions by the amplification of the GC-rich minisatellite and by the GC increase in nuclear CDSs is of special interest because it provides a model for the formation of the GC-rich and gene-rich isochores of the genomes of mammals and birds

Rule-based Out-Of-Distribution Detection

Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity. Results are available via open source and open data at the following link: https://github.com/giacomo97cnr/Rule-based-ODD

Rule-based Out-Of-Distribution Detection

Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity. Results are available via open source and open data at the following link: https://github.com/giacomo97cnr/Rule-based-ODD

Tegola tiered mesh network testbed in rural Scotland

Many rural and remote communities around the world see themselves on the wrong side of the digital divide. In particular, there is evidence to suggest that there is a growing digital divide between urban and rural areas in terms of broadband Internet access with people living in rural areas having fewer choices and pay higher prices for slower speeds. This is true even in developed countries. Motivated by the above observations, there has been an increasing interest in deploying and researching low cost rural wireless networks with active community participation. This paper presents an overview of our efforts in this direction in deploying a rural WiFibased long distance mesh network testbed in the Scottish Highlands and Islands. We highlight the unique aspects of our testbed that differentiate it from other existing rural wireless testbeds. We also outline some of the research issues that are currently being investigated in this project

Isolation and characterization of twelve microsatellite loci for the Japanese Devilray (Mobula japanica)

Twelve polymorphic microsatellites loci were characterized for Mobula japanica (Japanese Devilray) using an enrichment protocol. All but two loci were in Hardy-Weinberg equilibrium with no evidence of linkage disequilibrium or null-alleles for a sample of 40 individuals from two populations. The number of alleles varied from 5 to 28. Expected heterozygosity ranged from 0.2332 to 0.9589, making these microsatellite loci good candidates for population genetic studies

Ozone damage and tolerance in leaves of two poplar genotypes.

The effects induced by an acute ozone exposure were investigated in two poplar hybrids differen- tially O3 susceptible in terms of leaf injuries: Populus deltoides x maximowiczii, Eridano clone and..